The ability of a machine or computer to recognize an object has broad applications. These range from machine inspection or adaptive manufacturing processes to actually driving a vehicle without human intervention. The proposed invention utilizes unique approaches to recognition of road signs for a variety of purposes, including but not limited to the safety of the driver and other drivers, assistance seeing and recognizing road signs in obstructed conditions like fog or darkness, or assistance for visually impaired drivers.
The technical challenges of machine vision are known. Most systems use a sensor input such as a still image or video camera and then digitize or quantize the image into a set of numbers or vectors which may be easily manipulated and compared with other numerical representations or vector sets using a processor. The mathematical representation of the object to be recognized may be referred to a mask when expressed using the same mathematical process as the video or photographic image. The two representations, image and mask, may be compared mathematically.
Numerical correlation will occur as a result of processing the image data against the mask data whenever the image resembles the mask. A threshold is established for this measure of correlation such that correlation above this threshold level may be considered a match with the mask or target object. A variety of mathematical techniques may be used to determine correlation. The method for rendering a correlation result and the determination of the best threshold to define a match help to determine the accuracy or effectiveness of the vision recognition system.
Optical process is also possible, rather than pure digitized image signal processing. In this case optical transforms may be used and optically compared with optical masks that have been optically processed in a similar manner. Control of coherence, splitting and remixing of the illuminating light through the optical system make this feasible within the optical domain alone and without the use of computers or digitized image data.
These recognition techniques need not use visible light to facilitate object or mask recognition. For example, radar systems use non-visible electromagnetic radiation to illuminate and “see” a target. The radar sensor data may be compared with known mask data to identify targets. Ultrasonic and sonar imaging are equally analogous, with th basic concepts being similar.
The application that uses known road signs as targets for comparison in the vision system has significant benefits to drivers. For example, aging drivers may be able to supplement their own skill using such a system and drive safely for a longer period of their lives. When visibility is limited, or when signs are partially obscured, these vision recognition systems will likely to a better job of seeing and recognizing road signs that human drivers. Missed or misunderstood road signs are a significant cause of collisions worldwide.
The challenge in using a non-human system to recognize road signs, and then initiate planned events such as alarms, audio reading of the sign, or other actions, is being sure that the applicable sign is read and that the sign is read accurately. In this sense, it is important that the levels of correlation be adjusted to the right degree of recognition. This reduces false positive reads and also reduces missed signs, even when the input signal is minimal due to visibility or partial obstruction. Therefore, one aspect of the present invention is the provision of a non-human system that capable of complementing the system user while providing meaning correlation thresholds to determine accurate road sign readings.